S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Mechanical Aerospace Engineering (기계항공공학부) Theses (Master's Degree_기계항공공학부)
Minimization of Multi-Axis Interference for Fault Detection of Industrial Robots Based on Blind Source Separation
산업용 로봇 고장 진단을 위한 암묵신호 분리 기반 다축 간섭 최소화 기법
- Issue Date
- 서울대학교 대학원
- Fault detection; Industrial robot; Multi-Axis Interference; Signal separation; Independent component analysis
- 학위논문(석사)--서울대학교 대학원 :공과대학 기계항공공학부,2019. 8. 윤병동.
- As smart factory is becoming popular, industrial robots are highly demanding in many manufacturing fields for factory automation. Unpredictable faults in the industrial robot could bring about interruptions in the whole manufacturing process. Therefore, many methods have been developed for fault detection of the industrial robots. Because gearboxes are the main parts in the power transmission system of industrial robots, fault detection of the gearboxes has been widely investigated. Especially, vibration analysis is a well-established technique for fault detection of the industrial robot gearbox.
However, the vibration signals from the gearboxes are mixed convolutively and linearly at each axes, which makes it difficult to locate a damaged gearbox, and reduce fault detection performance. Thus, this paper develops a vibration signal separation technique for fault detection of industrial robot gearboxes under multi-axis interference. The developed method includes two steps, frequency domain independent component analysis (ICA-FD) and time domain independent component analysis (ICA-TD). ICA-FD is aimed at separating convolutive mixture of signals, and ICA-TD is aimed at eliminating the residual mixed components.
The experiment is performed to demonstrate the effectiveness of the proposed method. The results show that the proposed method could successfully separate the mixed signals by obtaining vibration signals from each gearbox, and enhance fault detection performance for the industrial robot gearboxes.